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Related Concept Videos

Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...
Prediction Intervals01:03

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
The...
Goodness-of-Fit Test01:16

Goodness-of-Fit Test

The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...

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Related Experiment Video

Updated: Jun 15, 2026

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Sensitivity analysis of informatively coarsened data using pattern mixture models.

Michelle Shardell1, Samer S El-Kamary

  • 1Department of Epidemiology and Preventive Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA.

Journal of Biopharmaceutical Statistics
|February 26, 2010
PubMed
Summary
This summary is machine-generated.

Sensitivity analysis using coarsened data models addresses potential bias from missing and interval-censored data. Pattern-mixture models allow for departures from the coarsening at random assumption in clinical trials.

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Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Data Analysis

Background:

  • Incomplete data, including missing and interval-censored data, is common in clinical trials.
  • Bias can arise from assumptions made about the missing data mechanism.

Purpose of the Study:

  • To introduce a framework for sensitivity analysis using coarsened data.
  • To address potential bias in randomized controlled trials with incomplete data.

Main Methods:

  • Utilized the framework of coarsened data.
  • Specified pattern-mixture models to generalize missing at random and independent censoring assumptions.
  • Applied these methods to a randomized controlled trial for acute hepatitis.

Main Results:

  • Demonstrated the application of pattern-mixture models for sensitivity analysis.
  • Quantified potential bias due to missing and interval-censored data.

Conclusions:

  • The coarsened data framework and pattern-mixture models provide a robust approach to sensitivity analysis.
  • This methodology is valuable for assessing the impact of missing data in clinical trials.